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1 Can migration prospects reduce educational attainments? * David McKenzie a and Hillel Rapoport b a Department of Economics, Stanford University, and World Bank Development Research Group b Department of Economics, Bar-Ilan University, CADRE, University of Lille II, and Stanford Center for International Development September 2005 Abstract: This paper examines the impact of migration on educational attainments in rural Mexico. Using historical migration rates by state to instrument for current migration, we find evidence of a significant negative (or disincentive) effect of migration on schooling levels of 16 to 18 year olds. This effect is strongest for males and for children of highly educated mothers. As a result of this, migration increases intergenerational mobility in education. However, these changes in mobility are driven mainly by reductions in schooling at the top of the education distribution, rather than by increases in schooling from relaxing liquidity constraints at the bottom. Our interpretation is that the illegal nature of the US-Mexico migration at low and intermediate levels of skills decreases the expected return to schooling for prospective migrants and is therefore the cause of the strong decrease in average education levels observed in Mexico's rural areas most affected by migration. Keywords: Migration, migration prospects, education attainments, Mexico JEL codes: O15, J61, D31 * Corresponding author: David McKenzie, Department of Economics, Stanford University, 579 Serra Mall, Stanford, CA , USA. We thank Thomas Bauer, Gordon Hanson, Ernesto Lopez-Cordoba, Francois-Charles Wolff, and participants at the Inter-American Development Bank's "Economic Integration, Remittances and Development" conference in Washington D.C., February 2005, and at the meeting of the European Society for Population Economics, Paris, June 2005, for useful comments on an earlier draft. 1

2 1. Introduction Education is a major developmental goal and, at the same time, a key input for development and growth. International agencies now actively encourage efforts to promote access to education for all (recall that universal primary education is one of the Millennium development goals) and governments in developing countries have designed programs such as the Progresa program in Mexico to increase education attainments in poor rural areas. On the other hand, the problem in developing countries is that many governments lack either the financial resources or the political will to meet their citizens' education needs. In practice, this means that education is often privately financed, with its direct costs (or user-payments) borne by the parents on the top of the foregone earnings associated to sending children to school (Hillman and Jenkner, 2004). In a context of poverty and in the absence of generous public education programs, credit constraints generally limit the education investment of households at the middle to lower end of the income distribution. This paper investigates the impact of migration on educational attainments in rural Mexico. Migration out of the rural areas of Mexico has long been and is maybe more than ever an essential element in poor households' survival and livelihood strategies. Every fifth household in rural Mexico has at least one member with international migration experience, and remittance income has been shown to contribute to relax credit constraints that impede investment in education, health, agricultural techniques, and other productive uses. However, identifying the effects of remittances on households education decisions is difficult since both the decision to migrate, and the decision among migrants of how much to remit, are likely to be related to education outcomes. This paper will instead estimate the overall impact of migration on education attainments in rural Mexico. This impact is composed of: the effect of remittances on the feasible amount of education investment (which is likely to be positive where liquidity constraints are bidding); the effect of having parents absent from the household as a result of migration (which may translate into less parental inputs into education acquisition and maybe into more house and farm work by remaining household members, including children); the effect of migration prospects on the desired amount of education (which depends on how education incentives are affected by the prospect of migration); and other potential spillover and general equilibrium effects. As we justify in greater length below, we focus on identifying the overall impact of migration on education rather than trying to estimate the effect of the different channels separately. Nevertheless, our findings point to a positive effect of remittances on schooling attainments for 12 to 15 year olds from relatively poor households, especially females, and to strong, negative substitution and incentives effects of migration on education of older children, and more so at the middle to upper end of the income and wealth (proxied by maternal education) distribution. The first of these results is in keeping with previous studies while to the best of our knowledge the second, pessimistic result had not been described so far in the literature. Indeed, previous literature has emphasized the positive relationships between migration and education. First, a number of recent empirical studies have explored the potential for remittance income and other spillover effects of migration to improve access to education for the poor. For example, Hanson and Woodruff (2003) used the 2000 Mexican Census to evaluate the effect of migration on accumulated schooling (number of school grades completed) by year-olds and found that children in households with a migrant member complete significantly more years of schooling, with an estimated increase that ranges from 0.7 to 1.6 years 2

3 of schooling, depending on age and gender. Interestingly, the gain is the highest for the categories of children traditionally at risk of being dropped from school (i.e., girls and 13 to 15-year olds). They interpreted this result as showing that remittances relax credit constraints on education investment, thus offsetting any possible negative impact on schooling of having a parent away from home. Cox Edwards and Ureta (2003) reached similar conclusions for El-Salvador. Their estimates of survival functions show that remittances significantly contribute to lower the hazard of leaving school. This effect appears greater in the urban areas, but the mere fact of receiving remittances (irrespective of amounts) is shown to have a very strong positive effect in the rural areas as well. Two very recent papers would seem to further confirm these positive effects of remittances on education attainments: Lopez Cordoba (2004) uses the 2000 Mexican census to examine relationships between remittances and various outcomes at the municipality level and finds that municipalities in Mexico which receive more remittances have greater literacy levels and higher school attendance among 6 to 14 year olds; and Yang (2004) finds greater child schooling in families whose migrants receive larger positive exchange rate shocks in the Philippines. In contrast to the above cited studies focusing on Mexico, we use a specific demographic survey of the Mexican population instead of Census data, which allows us to obtain a broader definition of a "migrant household". More importantly, we extend the analysis to include children aged 15 to 18 year-olds. Mexico s education system now provides for nine grades of compulsory schooling, and so in principle it is not until the age of 15 or 16 that children and their families begin making decisions about completion of non-compulsory grades. Furthermore, migration for work starts to become a possibility at this age, especially for males, while the absence of migrant parents may lead to children of this age being entrusted with household responsibilities which take the place of schooling. Second, while the first strand of the migration-education literature just surveyed focuses on the effect of past migration on current schooling, another strand of that literature, namely, the brain drain literature, has long recognized the possible link between expectations of future migration (or migration prospects) and current schooling decisions. The underlying assumption in much of this literature is that education is needed to migrate or at least raises an individual's chances to migrate, and since incomes abroad are much larger than at home, migration prospects raise the expected return to education and therefore induce higher domestic enrollment in schools (Bhagwati and Hamada, 1974, McCullock and Yellen, 1977). More recently, a series of theoretical models have emphasized that since the education decision is made in a context of uncertainty about future migration, part of the increased stock of human capital induced by the prospect of migration may actually remain in the home country so that on the whole, it can be that brain drain migration increases the stock of human capital at home even after actual emigration is netted out. This possibility has received empirical support from the first studies to investigate the brain drain-human capital formation relationship using cross-sectional data. 1 However, in the case of the Mexican migration to the United States, most first-time migration is illegal and, as such, involves no formal education requirement. In addition, as inequality is much greater in Mexico than in the U.S., one would expect higher returns to schooling in Mexico. Chiquiar and Hanson (2005) provide evidence that returns to education are indeed higher in Mexico than for Mexicans in the U.S. Taken together, these elements 1 See Commander, Kangasniemi and Winters (2004) and Docquier and Rapoport (2004) for surveys of this new theoretical and empirical brain drain literature. 3

4 give some indication that the possibly of migration may in fact lower the anticipated returns to education for prospective Mexican immigrants to the U.S. and, consequently, impact negatively on education investment in Mexico. The aim of this paper is to explore how the positive and negative effects of migration on the level of education combine together, and to do so using a dynamic perspective that accounts for the fact that migration is likely to affect education outcomes differently in low and high-migration communities. Section 2 presents the dataset used for the empirical analysis, namely, the National Survey of Demographic Dynamics (Encuesta Nacional de Indicadores Demográficos ENADID), and contrasts it to the 2000 Mexican Census which has so far been the dataset predominantly used by previous studies on migration and education attainments in Mexico. In Section 3 we first explain why it is difficult to disentangle the effects of remittances on households' education decisions from the effects of other channels through which migration affects education; we then discuss why research attention should focus on the overall impact of migration rather than at trying to find only the effect of remittances and, in so doing, we provide justification for the identification strategy retained. Section 4 presents a theoretical framework where the main effects of migration on the feasible and desired amount of education combine together at different wealth levels. The results are presented in Section 5. After using historic migration networks to instrument for the current migration status of a household, we find no significant impact of migration on schooling of 12 to 15 year olds but find a significant negative impact on the years of schooling completed by 16 to 18 year olds, with more of a reduction for males and for children with more highly-educated mothers. As a result of this, migration is found to increase intergenerational mobility in education for both males and females. However, these changes in mobility are driven mainly by reductions in schooling at the top of the education distribution, rather than by increases in schooling from relaxing liquidity constraints at the bottom. On the whole, the results point to the effect of potential migration on the incentives to complete schooling as being more important than any relaxation of credit constraints on schooling. Our interpretation of these findings is that the illegal nature of the US- Mexico migration at low and intermediate levels of skills coupled with the steeper skills-wages profile in Mexico explain the decrease in the expected returns to schooling for prospective migrants and are therefore the cause of the fall in average education levels in the communities most affected by migration. Section 6 concludes. 2. Data This paper uses data from the 1997 Encuesta Nacional de la Dinámica Demográfica (ENADID) (National Survey of Demographic Dynamics) conducted by Mexico s national statistical agency (INEGI) in the last quarter of The ENADID is a large nationally representative demographic survey, with approximately 2000 households surveyed in each state, resulting in a total sample of 73,412 households. We restrict our analysis to rural communities, defined broadly here to be municipalities which are outside of cities of population 100,000 or more and where at least 50 households were surveyed. 3 This gives a sample of 214 rural municipalities 2 Survey methodology, summary tables, and questionnaires are contained in INEGI (1999). 3 Our main results are robust to lowering this threshold to cities with population below 50,000 or 15,000. 4

5 across all Mexican states. Within these communities we have a sample of 26,197 households, of which 9,758 households contain at least one child aged 12 to 18 years. The ENADID asks whether household members have ever been to the United States in search of work. This question is asked of all household members who normally live in the household, even if they are temporarily studying or working elsewhere. Additional questions ask whether any household members have gone to live in another country in the past five years, capturing migration for study or other non-work purposes in addition to work related migration. We define a household as having a migrant if they have a member aged 19 and over who has ever been to the U.S. to work, or who has moved to the U.S. in the last five years for any other reason. We use a cutoff of age 19, rather than the age 15 threshold proposed by Massey, Goldring, and Durand (1994) in order to exclude from the migration network children aged for whom we will be looking at education outcomes. As there are relatively few migrants in this age group, our results are robust to the use of this lower threshold. Table 1 provides summary statistics for the key variables used in this study. Almost one quarter of all households in our sample with a child aged 12 to 18 have a migrant member. 4 Households with secondary school-aged children are more likely to have a migrant member than the general population: the migration rate is 16 percent in households without a child aged 12 to 18. The ENADID questions on migration within the last five years are identically worded to those used in the 2000 Mexican Census, which does not capture migration by household members outside of a fiveyear window. Table 1 shows that relying on the Census questions to define migrant status understates the proportion of households with migrant experience by almost fifty percent. Conversely, one in eight households classified by the Census definition as not having a migrant have a member who has ever been to the U.S. to work. Examining migration within the last five years is likely not to be unduly restrictive for certain types of analysis. However, there are number of reasons to prefer looking at whether household members have ever migrated in examining the impact of migration on education. Schooling is a cumulative process, with each year building on the year before. Any impact of migration on schooling during the years of primary education may therefore affect schooling six to ten years later. A portion of this effect may be at the extensive margin: 10 percent of 12 year olds in our sample are not currently attending school, which makes it likely they will not be attending school at age 18. There are also likely to be effects at the intensive margin, whereby household resources and effort devoted to schooling during primary school affect the ability of children to continue schooling in later years. In addition to these direct effects through prior schooling, migration by household members six or more years ago may still result in higher household wealth today, influencing the ability to pay for schooling later on. Furthermore, as will be outlined in detail below, schooling decisions may depend on the expectation of migration in the future. This expectation will depend in part on previous household migration experience, whether or not the 4 The sample proportion is The survey provides sample weights designed for the purpose of obtaining state-level rates of demographic indicators, and using these weights gives a proportion of After we restrict our sample to rural households in communities with more than 50 households which have secondary school-aged children, the sample weights provided are not designed to provide population estimates for this population, and so for the remainder of our analysis we do not use the population weights, report results for the large sample we have. 5

6 migration episodes occurred within the last five years. For these reasons we prefer the ENADID to the Census for examining the effects of migration on education. The ENADID asks migrants who have ever been to the U.S. for work a set of additional questions about their migrant experience, including the number of trips they have ever made, and whether they had legal documentation to work. Approximately 50 percent of all migrants have made more than one trip, with a mean of 2.8 trips per migrant. The vast majority of migrants in our sample had no legal documentation to work, especially on their first trip. Over 91 percent of first-time migrants who went to work in the U.S. had no legal documentation to do so. One downside of the ENADID is that the information it collects on remittances is not as comprehensive as that collected in some other sources of Mexican migration information, such as the Mexican Census. In addition to separate questions on labor income, the ENADID asks each individual whether they have received income in the past year from pensions, transfers from relatives within the country, transfers from relatives outside the country (remittances), rent, interest, scholarships, the Procampo program, and other sources. The interviewer reads this list of eight categories, and records up to two sources per individual. Therefore remittance income may or may not be collected for any individual receiving income from at least two other categories from this list, leading to an under-recording of remittance income. While it is difficult to gauge the exact extent or biases introduced by this underreporting, comparisons with the Mexican Census numbers reported by Hanson and Woodruff (2003) suggests an undercount of approximately 15 to 20 percent in the proportion of migrant households receiving remittances. 5 As this paper will argue, migration affects educational outcomes in a number of ways, of which current remittances received is only one part. It is difficult to think of variables which are not correlated with education decisions that allow one to identify why one migrant household will receive remittances and another will not, or why one migrant sending remittances sends more remittances than another migrant also sending remittances. For these reasons this paper will focus on the impact of migration, rather than of remittances per se. Our main measure of education is based on years of schooling attained by children and adults. Elementary education (grades 1 to 6) is compulsory in Mexico and is normally provided to children aged 6 to 14. Lower secondary education (grades 7 to 9) became compulsory in 1993 and is generally given to children aged 12 to 16 years who have completed elementary education. This is followed by three years of upper secondary schooling (grades 10 to 12) and higher studies. Despite education being compulsory, there is still far from complete compliance and a lack of infrastructure in some of the more rural areas (SEP, 1999). Approximately half of all 15 year olds with less than 9 years of attained schooling were not attending school in We focus our study on children aged 12 to 18, the ages at which children will be receiving the majority of their post-primary education, and the age range at which children start leaving school. 89 percent of 12 year olds in our sample were attending school in 1997, compared to 57 percent of 15 year olds and 26 percent of 18 year olds. 5 A first pass is to compare our results from Hanson and Woodruff (2003), who report that 38.2 percent of migrant households with children aged 10 to 15 receive remittances. Using the census definition of migrant status, the corresponding number is 28.6 percent for our sample, and 31.6 percent if we restrict our sample to communities of population size less than 15,000 as they do. 6

7 Table 2 provides a first exploration of the association between child schooling attainment and migration. We first test for a difference in mean years of schooling attained by age for males and females. There is no significant difference in mean years of schooling between boys aged 10 to 14 in migrant and non-migrant households, while boys aged 15, 17 and 18 living in migrant households have significantly lower mean schooling levels. The ENADID asks about all household members who usually live with the household, even if they are absent due to study or work, so these differences are not due to more educated boys in migrant households being absent from the household. On average, 16 to 18 year old boys in migrant households have accumulated one-third of a year less schooling than boys in nonmigrant households. The only significant difference, at the 10 percent level, between migrant and non-migrant household in girls schooling occurs for girls aged 12 and 13, who receive 0.15 to 0.20 years more schooling in migrant households. Hanson and Woodruff (2003) find that the effects of migration on schooling of 10 to 15 year olds in the Mexican Census vary according to the level of maternal schooling. In our sample we find 11.5 percent of children aged 12 to 18 in migrant households do not have data on their mother s education, compared to 13.1 percent of children aged 12 to 18 in non-migrant households. 6 In the bottom half of Table 2 we test for differences in mean years of schooling for those children for whom maternal education is available. We present results by three groups of maternal education: 0 to 2 years (34 percent of mothers), 3 to 5 years (26 percent of mothers), and 6 or more years of education (40 percent of mothers). There is no significant difference between migrant and non-migrant households in mean years of schooling for boys with loweducated mothers, whereas girls in migrant households with mothers with 0 to 2 years of schooling have 0.38 to 0.47 more years of schooling than girls in non-migrant households with low-educated mothers. In contrast, we find migration to be associated with significantly lower levels of schooling of i) 0.42 to 0.55 years for boys aged 16 to 18 whose mothers have 3 or more years of education; ii) 0.43 years for boys aged 12 to 15 whose mothers have 6 or more years of education; and iii) 0.55 years for girls aged 12 to 15 whose mothers have 6 or more years of education. 3. Identification strategy Remittances are perhaps the most tangible consequence of migration and therefore it is not surprising that there has been a large research interest on the effects of remittances on receiving households in developing countries (Rapoport and Docquier, 2005). Two main approaches have been employed in the literature. The most basic descriptive approach asks households what remittances are spent on or the purpose they are intended for. 7 However, resources are fungible, which renders the exact use of a particular income source almost intractable. Therefore the second approach used is to examine an outcome of interest, say education, by comparing households which receive remittances with households which do not. One branch of literature 8 assumes that all the systematic differences between remittance receiving and non-receiving households can be explained by a set of characteristics of the migrant's receiving 6 This difference is statistically significant at the 1% level. For 16 to 18 year olds, we are missing maternal education data for 15.8 percent of children in migrant households and 18.6 percent of children in non-migrant households. 7 See Durand and Massey (1992) for a review of such studies in the case of Mexican migration. 8 Some examples include Adams (1991), Taylor and Wyatt (1996) and Cox-Edwards and Ureta (2003). 7

8 household and community and then estimates the impact of remittances on an outcome of interest through OLS regression. The main problem is that households which receive remittances differ along both observable and unobservable dimensions from those households which do not. Moreover, in providing coinsurance to household members, migration in and of itself may allow households to engage in new investments (e.g., education) with no need for remittances to occur. To circumvent these difficulties, one may instead replace the explanatory variable "remittances" by the variable "migration" and estimate the impact of having a migrant member. For example, the simple comparisons of means in our Table 2 find some significant differences in educational attainment between children in migrant and nonmigrant households. It is therefore tempting to check whether such significant differences remain after controlling for household and community characteristics. This approach, however, is not very satisfactory because if the two groups of households (remittance-receivers and non-receivers, migrants and non-migrants) are really the same after controlling for observable differences, they should have the same migration and remittance behavior (Lalonde and Topel, 1997). In particular, one is usually concerned that whether or not a household receives remittances, or whether or not a household has a migrant member, may be correlated with unobserved variables which also affect the outcome of interest. There are two main categories of concern: unobserved shocks, and unobserved attributes of the household. As an example of the first concern in the case of the migration-education relationship, Hanson and Woodruff (2003) note that negative labor market shocks experienced by parents may both induce migration and require children to work instead of spending time in school, leading to a spurious negative relationship between migration and years of schooling. The second concern is that households which receive remittances or have a migrant member differ in terms of motivation, ability, concern for their children, and other such hard-to-measure attributes. For example, parents who care more strongly about the education of their children may migrate in order to earn income that can be used to pay for schooling expenses, and will also devote more attention and nonincome resources to improving schooling outcomes of their children. A simple comparison of migrants and non-migrants would in this case overstate the education gains from migration. In such a case, estimation of an education equation through OLS regression would overstate the impact of migration on schooling. In short, the results would be biased in a way or another, with the direction of any selectivity bias being theoretically uncertain. One solution to this problem is to employ the method of instrumental variables. The idea is to find a variable (the instrument) which helps predict either remittances or migration, but does not otherwise impact on the outcome of interest. There is a sizeable literature which looks at the empirical determinants of remittances and migration. However, most of the variables which help predict whether a household member migrates, or whether a household receives remittances, are likely to also impact on its education decisions. For example, the household head s age and education, and the income and demographic composition of a household may help predict whether they receive remittances or send members out, but will also affect the education of their children and other outcomes of interest. Such variables therefore belong in the set of controls to be included in the education equation and can not serve as instruments. 8

9 We therefore follow Woodruff and Zenteno (2001) and a number of subsequent studies 9 in using historic state-level migration rates as an instrument for current migration stocks. In particular, we use the U.S. migration rate from 1924 for the state in which the household is located, taken from Foerster (1925) 10. These historic rates can be argued to be the result of the pattern of arrival of the railroad system in Mexico coupled with changes in U.S. demand conditions for agricultural labor. As migration networks lower the cost of migration for future migrants, they become self-perpetuating. Hildebrandt and McKenzie (2004) show that the historic migration rate is a strong predictor of current migration rates, with a first-stage F- statistic of over 30. Our identifying assumption is then that historic state migration rates do not affect education outcomes over 70 years later, apart from their influence through current migration. Instrumental variables estimation relies on this exogeneity assumption, and so it is important to consider and counteract potential threats to its validity. One potential threat is that historic levels of inequality and historic schooling levels helped determine migration rates in response to the railroad expansion, and also influence current levels of schooling due to intergenerational transmission of schooling. To allow for this possibility we control for a number of historic variables at around the same time period as our historic migration measure. The controls are the proportion of rural households owning land by state in 1910 taken from McBride (1923) 11 ; and the number of schools per 1000 population by state in 1930, and male and female school attendance for 6 to 10 year olds by state for 1930, both taken from DGE (1941). A second possible threat to validity is that the development of the railroads in certain states and communities ushered in the subsequent development of other infrastructure, such as school facilities, and led to changes in the income distribution which themselves influenced the incentives and ability to invest in schooling. We include the following state-level controls for this possibility, all calculated from the public use sample of the 1960 Mexican Census: the Gini of household income, the Gini of years of schooling accumulated for males and females aged 15-20, and the average levels of years of schooling accumulated for males and females aged Spearman rank-order correlation tests do indeed indicate some significant correlations between the 1924 migration rates and some of these controls: states with higher historic migration rates had higher average rates of schooling and lower inequality in schooling in This might represent the influence of migration over the period, or the effects of concomitant trends, and so we prefer to include these 1960 education inequality and levels as controls. Even after controlling for these variables, historic migration rates remain a powerful predictor of current community migration prevalence, with a first-stage F-statistic of 28. Historic migration rates can therefore be used as an instrumental variable to determine the impact of migration on education outcomes. Can we then also use historic migration networks to identify the causal impact of remittances on education? This would require assuming that historic migration networks affect education outcomes only through remittances. Since we have argued that historic migration networks help predict current migration, this amounts to assuming that the only 9 Hanson and Woodruff (2003); McKenzie and Rapoport (2004); López-Córdoba (2004); and Hildebrandt and McKenzie (2004) all employ historic migration rates as instruments for current migration. 10 Thanks to Chris Woodruff for supplying these historic rates. 11 Land ownership data were kindly provided by Ernesto López-Córdoba. 9

10 impact of migration on education is through remittances, which does not appear to be a tenable assumption. Identifying the effect of remittances, as distinct from the overall impact of migration, therefore involves a second level of complexity. We must not only find a variable which helps determine why one household migrates and another with similar observable characteristics does not, but also find a variable which can explain why one family with a migrant household member receives more remittances than another similar family which also has a migrant member. Variables which help predict migration, such as migrant networks or institutional arrangements such as migrant quotas, do not appear to be suitable for predicting why one migrant will send more remittances than another similar migrant. Therefore, in this paper we will use historic migration networks as an instrument for migration; this will enable identification of the overall impact of migration, and allow us to show that migration has some effects on education which are clearly not due to remittances. 4. Theoretical framework We now turn to an examination of the impact of migration on the schooling of children. Let r i,s denote the present discounted value of the additional returns to child i of completing schooling year s, c i,s denote the additional financial costs of the child completing this additional year of schooling, and k i,s denote the additional nonfinancial costs of the child completing this additional schooling year, such as foregone income and the disutility of school effort. Costs are realized at the moment of schooling whereas returns are not realized until the future. Financial costs of schooling must therefore be met out of the household s current resources. The household s schooling decision is then to choose s {0,1,2,,N} to maximize the net present discounted value of schooling, subject to the condition that total financial schooling costs must be met out of current household resources net of subsistence needs, A i. That is, s * i s { 0,1,2,..., N} s ( ri, j ci, j ki, j ) s. t. = arg max c A (1) j= 1 s j= 1 i, j Let s i U denote the unconstrained optimal level of education for child i, which occurs when the financing constraint does not bind. We expect this to be weakly increasing in mother s education and household resources due to the possibility of more educated mothers lowering the disutility and non-financial costs of schooling by placing higher emphasis on education, helping with schoolwork, and perhaps due to a genetic ability component. The returns to schooling may also be higher for richer households due to peer effects and the ability to enter occupations with high start-up costs. Denote by s i P the maximum possible years of schooling the household can afford under its budget constraint. This is clearly increasing in wealth, and is likely to be increasing in maternal education since household resources are likely to be correlated with mother s schooling. Then: i U P ( s s ) s * = min, (2) i i Figure 1 then illustrates the relationship between s i * and household wealth levels or maternal education. Child schooling is predicted to increase with household resources, both due to relaxing of credit constraints and to the possible higher desired levels of education for children in richer households with more educated mothers. i 10

11 Now consider the potential impacts of migration on a household s optimal education. The most direct effect of migration on schooling is likely to be through its effect on household resources. Remittances and potentially higher earnings after migration (such as from entrepreneurship, see Woodruff and Zenteno, 2001) increase the value of household resources, increasing the maximum years of schooling the household can afford, s i P. Figure 2 illustrates the case when this is the only effect of migration on child schooling. The relaxation of credit-constraints allows poorer households to move to or towards their unconstrained optimal level of education, resulting in higher education for their children. In contrast, as children in rich households were never constrained, there should be no effect on their schooling. As a result, the direct effect of remittances should reduce education inequality and increase intergenerational mobility in schooling. 12 However, migration is likely to have a number of other effects on child schooling in addition to its effect through remittances. Hanson and Woodruff (2003) note that one potential negative effect is that migration may disrupt household structure, removing children from the presence of guardians and role models, and require older children to take on additional household responsibilities. In our model this can be thought of as increasing the non-financial costs of schooling, k i,s, leading households to lower s U i, their unconstrained level of education. A further effect which we wish to consider is the possibility that due to information and network effects, having a migrant parent increases the likelihood that the children themselves will become migrants. This may have an immediate substitution effect, whereby as a result of the opportunity cost of staying in school increasing due to higher potential earnings abroad, children drop out of school in order to work. Again this can be viewed as increasing k i,s, leading households to lower s U i. There appears to be some direct substitution, particularly for males. Indeed, 7.5 percent of males aged 16 to 18 in households with an older migrant member have themselves migrated to work, compared to 0.8 percent of males in this age group in non-migrant households. As 70 percent of males aged 16 to 18 in migrant households are not attending school, compared to 61 percent in non-migrant households, this direct substitution can potentially account for a large portion of the difference in school attendance. The extent of migration is much lower for females, with only 1.3 percent of 16 to 18 year olds in migrant households and 0.2 percent in non-migrant households having themselves migrated to work. Even if children do not migrate at the age when they would be attending school, the possibility they may migrate in the future can influence the expected returns to education, changing r i,s in our framework. Recent theoretical literature has emphasized that the possibility of migration in the future may increase the returns to schooling, leading to more human capital investment. However, as discussed in the introduction, returns to schooling appear to be higher in Mexico than in the United- States, inasmuch as a substantial fraction of Mexican immigrants enter illegally. As a result, the possibility of migration in the future will lower the expected returns from education. Since children of migrants are more likely to migrate in the future than U children of non-migrants, we would therefore expect this incentive effect to lower s i 12 Remittances and higher post-migration incomes on the part of parents may also allow their children to overcome credit constraints involved in entering certain occupations, thereby potentially increasing the returns to education. These effects are most likely for children in poor households, and will therefore have qualitatively the same implications for schooling levels and inequality as when remittances operate through lowering credit constraints on schooling. 11

12 in migrant households. To the extent that network effects operate also at the community level, we would expect this disincentive effect to be stronger in lowmigration costs (large migrant networks) communities. 13 Each of these three additional channels through which migration may affect child schooling (disruption of household structure, direct substitution of schooling today for migration today, and the change in expected future returns to education) all act to lower s U i. Assume first that this reduction in the unconstrained desired level of schooling occurs equally across wealth and maternal education levels. Coupling this with the increase in s P i arising from remittances gives an overall effect of migration as seen in Figure 3. Two possibilities arise. Figure 3a shows the case where the effect of alleviating credit constraints outweighs the reduction in desired schooling levels for the poor, so that child schooling increases in poor households. In contrast, unconstrained households only experience the effects of reductions in desired schooling, and so schooling falls. Figure 3b shows the case in which the fall in desired schooling is sufficiently large that no household would have been credit-constrained, even in the absence of remittances. In this case, schooling falls for all wealth levels after migration, but still should fall by more for richer households. In either case we would expect to see a reduction in education inequality and increase in education mobility following migration. Nevertheless, under some circumstances one might anticipate seeing more of a reduction in schooling among poorer households following migration, and a corresponding increase in education inequality and reduction in intergenerational education mobility. Figure 3c outlines one such scenario. Basic education is provided free by the state along with free textbooks (SEP, 1999). Along with a number of targeted programs towards the poor, it is likely that even the poorest Mexican households have sufficient household resources to afford some years of post-primary schooling. It is therefore possible that desired schooling levels lie below possible schooling. Migration may then lower s U i by more for poorer households than for richer households. This is particularly likely in communities with large migration networks, where the poor have higher probabilities of migration. The education of children in poorer households may also suffer more from the absence of parents, due to a higher likelihood of having to help out in family businesses or look after siblings. As a result, the desired schooling level may decrease by more in poorer households than in richer households, thereby increasing education inequality. 5. Results The theoretical impact of migration on education is therefore unclear, as is likely to depend on household resources. However, the ENADID only contains measures of current household assets, which are themselves affected by the household s migration decision. We therefore instead examine how the impact of migration varies according to maternal education. There is a large literature which finds that higher maternal 13 An offsetting effect in poor households might be that children who anticipate migrating in the future might invest more than they otherwise would in education, in order to earn enough in Mexico to pay for the up-front costs of migrating. Such an effect may be strong for particular individuals in communities with small networks who have high expectations of migrating. However, for the average individual this effect may be small, since individuals in high network communities, with high probabilities of migrating, face low migration costs, while individuals facing high migration costs in low network communities have much lower probabilities of migrating. 12

13 education is associated with more education of future generations. Moreover, as discussed above, many of the interactions between household wealth and migration status in determining the impact of migration on schooling are likely to apply for maternal education as well. Furthermore, maternal education and household wealth are strongly correlated in our sample. Mother s years of schooling has a 0.46 correlation with an asset index formed as the first principal component of a number of asset indicators Educational attainments We estimate the following equation for S i,c, the years of schooling completed by child i in community c: S i, c = λ + λ Mig 0 i, c + α MidEduc λ Mig i, c 2 i, c MidEduc + α HighEduc 2 i, c i, c + φ' X + λ Mig i, c 3 + γ ' Z i, c c HighEduc + ε i, c i, c (3) where Mig i,c is a dummy variable taking the value one if child i lives in a household with a migrant member, MidEduc and HighEduc are dummy variables for child i having a mother with 3-5 years of schooling and 6 or more years of education respectively, X i,c is a vector of child controls, such as age and age squared, and Z c is a vector of state-level controls. Equation (3) is estimated separately for four groups: males 12 to 15, males 16 to 18, females 12 to 15, and females 16 to 18. For each group we estimate equation (3) with and without the controls for maternal education. OLS results with and without interactions of migrant status with maternal education are compared with two-stage least squares results in which the 1924 state-level migration rate and its interactions with maternal education are used as instruments for whether a household has a migrant. Since this instrument only varies at the state level, we cluster our standard errors at the state level to allow for arbitrary correlation in the error structure of individuals within a state. This approach follows closely the work of Hanson and Woodruff (2003). The two main differences are that we use the ENADID rather than the Mexican census, allowing us to classify households according to whether they have ever had a migrant, rather than on whether they have had a migrant in the last five years; and that we also consider 16 to 18 year olds. It is this latter group whom we think the incentive effects and direct substitution effects of migration on schooling will potentially be the strongest. Table 3 presents the results of this estimation for males. Looking first at 12 to 15 year olds, we see in columns 1 and 3 that the overall impact of migration is small and insignificantly different from zero. Columns 2 and 4 find a relatively large increase in years of schooling associated with maternal education: boys in a nonmigrant household with a mother with 3 to 5 years of schooling have 0.54 to 0.66 years more schooling than boys in non-migrant households with a mother with 2 or fewer years of schooling, while boys with mothers with 6 or more years of schooling have 1.54 to 1.59 more years of schooling accumulated. This is a sizeable increase on the 5.4 mean years of schooling for boys whose mothers have two or fewer years of education. The interactions between mother s education and being in a migrant household are negative, and when coupled with the negative coefficient on migration status, suggest migration reduces education for boys with more highly educated 13

14 mothers. However, after instrumenting for migration status, these effects are insignificant. The results for 16 to 18 year olds males in Table 4 show a stronger impact of migration. Pooling across levels of maternal education results in an overall negative impact of migration, which becomes stronger after instrumenting. Being in a migrant household lowers average years of schooling by 1.4 years. Again we find higher levels of education for boys with more educated mothers, with the effects larger than for 12 to 15 year olds. After instrumentation we find that the interaction effects between education and migration status are negative, and significant for boys with more highly educated mothers. The coefficient on being in a migrant household is also negative and significant, so migration lowers education for boys whose mothers have less education, and lowers it by even more for boys with more educated mothers. In terms of the coefficients in equation (3), the overall impact of migration is λ 1 for children whose mothers have 0 to 2 years of education, λ 1 + λ 2 for children with maternal education of 3 to 5 years, and λ 1 + λ 3 for children with maternal education of 6 or more years. The foot of Table 4 reports p-values for Wald tests of significance of these effects. Migration lowers schooling by more both in absolute and in relative terms for boys with higher maternal education. Migration lowers schooling by 3.05 years for boys with maternal education of 6 or more years. This represents a 33 percent drop compared to the 9.14 mean years of education for boys in non-migrant households with highly educated mothers, and completely erases the 2.8 years of educational gain associated with having a highly educated mother. In contrast, the 0.94 fall in years of schooling for boys with low-educated mothers, and 2.02 fall in schooling years for boys with mid-educated mothers represent falls of 14 percent and 26 percent compared to the mean schooling levels for boys in non-migrant households with these education levels. Table 4 presents the estimates of equation (3) for females. The overall impact of migration is found to be insignificant when we pool girls with different levels of maternal schooling. This is the case for both 12 to 15 year olds and 16 to 18 year olds. The OLS results which allow for interactions with maternal education (columns 2 and 6) show effects of migration in line with the predictions of Figure 2 for year olds. Migration is associated with higher levels of education for girls whose mothers have 0-2 years of education, and has no effect on education for girls whose mothers have 3 to 5 years of education. However, these results change after we instrument for migration status. There is no significant impact of migration on education for girls 12 to 15, regardless of maternal education level. Migration is found to significantly lower education for girls aged 16 to 18, even in households with low maternal education. As with 16 to 18 year old boys, migration lowers education more for 16 to 18 year olds girls with more highly educated mothers. The increases in years of schooling associated with higher maternal education in non-migrant households are similar in magnitude to those found for boys. Hanson and Woodruff (2003) find a significant positive effect of migration on education of 13 to 15 year olds whose mothers have two or fewer years of education, and no effect for girls of this age whose mothers have higher education, or for males aged 13 to 15. Our results for 12 to 15 year olds broadly match their findings: we find no significant impact of migration on the schooling of boys aged 12 to 15, a positive impact on girls with low maternal education which is significant in our OLS estimation but insignificant after instrumenting, and insignificant effects on other 12 to 15 year old girls. Stronger results are found for the older 16 to 18 year old age 14

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